The long term objective of this project is to develop powerful and computationally efficient statistical methods of identifying genes underlying complex genetic traits in humans. The specific aims include development of methods for mapping loci responsible for dichotomous traits (such as affected or unaffected with a disease), quantitative traits (such as blood pressure), and ordered categorical traits (such as unaffected, mildly affected, and severely affected). All these methods will take full advantages of the existence of an increasingly dense genetic map of highly polymorphic DNA markers. The project will also investigate the power and efficiencies of these methods, and compare them with existing methods. In addition, this project will develop computer programs in order to implement the proposed methods, to perform several forms of data analysis, and to evaluate the performance of these methods through simulation and application to real data on familial psoriasis and non-insulin diabetes mellitus (NIDDM). The proposed methods are based on the notion that, for traits that are partly or completely genetically determined, the fundamental basis of phenotypic similarity among relatives is the sharing of the same genes. This sharing of the same gene(s), or the genetic similarity among relatives, can be detected through the use of efficient statistical mapping methods in conjunction with sufficient DNA marker information, regardless as to whether or not the environment may also play a role in phenotypic similarity. Efficient methods will be developed for computing the expected proportion of genes shared identical-by-descent (IBD), at one or two loci or in a chromosomal region by two or more relatives, conditional on observed marker information. On this basis, statistical methods for mapping the loci for dichotomous, ordered categorical, and quantitative traits will be developed. Several datascts are or will be available, including a large collection of pedigrees collected through the collaboration of the P.I. in a linkage study of familial psoriasis, and a large collection of pedigrees to be collected by an international consortium on identification of genes for NIDDM. Analyses include application of the proposed methods to 1) identify possible location(s) and mode of inheritance through visual exploratory analysis based on multipoint marker data, 2) map disease gene(s) to one or more narrow chromosomal segments flanked by DNA markers, 3) map genes underlying the quantitative traits, such as insulin resistance, to one or more narrow chromosomal segments, 4) localize categorical trait loci to one or more narrow chromosomal segments. The performance of these methods will be evaluated tbrou simulation studies as well as through application to real data sets.